The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
Computational solutions to large‐scale data management and analysis
Open accessAbstract
Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist - such as cloud and heterogeneous computing - to successfully tackle our big data problems.
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“The exponential increase in the volume of genomic data due to advancements in sequencing technologies necessitates WGA algorithms that can handle large datasets efficiently and accurately [76].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [76]
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“While training modules and review papers exist that describe typical workflows or common challenges and solutions for life scientists working with large, complex datasets (Schadt et al., 2010), these resources fall short in that they do not simultaneously offer step-wise, reproducible, and practical guidance.”
From Student‐led workshops: Filling skills gaps in computational research for life scientists (2021) · cites this paper as Schadt et al., 2010
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Citations
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Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.12.031 · Cited by 1,905
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
A window into third-generation sequencing
Schadt, E. E., Turner, S., & Kasarskis, A. (2010) · Human Molecular Genetics · doi:10.1093/hmg/ddq416 · Cited by 1,026
Self-citationThe Origin of the Haitian Cholera Outbreak Strain
Chin, C.-S., Sorenson, J. M., Harris, J. B., Robins, W. P., Charles, R. C., Jean‐Charles, R. R., Bullard, J., Webster, D. R., Kasarskis, A., Peluso, P. S., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1012928 · Cited by 747
Self-citation
Metadata
- Authors
- Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P.
- Affiliations
- Pacific Biosciences (United States) (US)
- Stanford University (US)
- Corresponding author
- Schadt, Eric E.
- Venue
- Nature Reviews Genetics
- Published
- 2010-09
- Volume
- 11
- Issue
- 9
- Pages
- 647-657
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3124937
- OpenAlex
- W2023116290
- Cited by
- 631
- References
- 42
- Citations by year
- 2026: 7 · 2025: 21 · 2024: 22 · 2023: 21 · 2022: 22 · 2021: 29 · 2020: 26 · 2019: 27 · 2018: 60 · 2017: 43 · 2016: 62 · 2015: 70 · 2014: 77 · 2013: 63 · 2012: 42
- FWCI
- 18.17
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (HHSN268201000034C)
- National Cancer Institute (R01CA130826)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- large-scale data management, high-dimensional data analysis, cloud computing, heterogeneous computing, genomic data, sequence data, bioinformatics, computational environment
- MeSH terms
- Animals, Humans, Sequence Analysis, DNA, Computational Biology, Genomics
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